Overview

Dataset statistics

Number of variables8
Number of observations12344
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory771.6 KiB
Average record size in memory64.0 B

Variable types

Numeric8

Alerts

per_area_buildings is highly overall correlated with per_area_greenery and 2 other fieldsHigh correlation
per_area_greenery is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
per_residential_road is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
per_rural_road is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
publictransport_frequency has 3604 (29.2%) zerosZeros
per_area_greenery has 128 (1.0%) zerosZeros
per_area_water has 1376 (11.1%) zerosZeros
per_residential_road has 278 (2.3%) zerosZeros
per_rural_road has 4332 (35.1%) zerosZeros
per_highway has 10440 (84.6%) zerosZeros
per_active has 5594 (45.3%) zerosZeros

Reproduction

Analysis started2024-07-05 13:41:41.459274
Analysis finished2024-07-05 13:41:51.839451
Duration10.38 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

publictransport_frequency
Real number (ℝ)

ZEROS 

Distinct3442
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1245.6848
Minimum0
Maximum90734
Zeros3604
Zeros (%)29.2%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-07-05T15:41:52.038991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median425
Q31348.25
95-th percentile5122.8
Maximum90734
Range90734
Interquartile range (IQR)1348.25

Descriptive statistics

Standard deviation2753.8593
Coefficient of variation (CV)2.2107193
Kurtosis201.48161
Mean1245.6848
Median Absolute Deviation (MAD)425
Skewness9.7296631
Sum15376733
Variance7583741.3
MonotonicityNot monotonic
2024-07-05T15:41:52.405494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3604
29.2%
240 20
 
0.2%
168 19
 
0.2%
742 19
 
0.2%
336 18
 
0.1%
24 18
 
0.1%
120 17
 
0.1%
78 17
 
0.1%
108 17
 
0.1%
156 16
 
0.1%
Other values (3432) 8579
69.5%
ValueCountFrequency (%)
0 3604
29.2%
2 13
 
0.1%
3 4
 
< 0.1%
4 5
 
< 0.1%
5 2
 
< 0.1%
6 11
 
0.1%
8 12
 
0.1%
9 9
 
0.1%
10 12
 
0.1%
11 7
 
0.1%
ValueCountFrequency (%)
90734 1
< 0.1%
83600 1
< 0.1%
56053 1
< 0.1%
47146 1
< 0.1%
45156 1
< 0.1%
37609 1
< 0.1%
35017 1
< 0.1%
33492 1
< 0.1%
31724 1
< 0.1%
30556 1
< 0.1%

per_area_greenery
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12217
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.36656
Minimum0
Maximum38.234096
Zeros128
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-07-05T15:41:52.589917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.0023607
Q14.7291863
median9.7319415
Q322.091389
95-th percentile32.310119
Maximum38.234096
Range38.234096
Interquartile range (IQR)17.362203

Descriptive statistics

Standard deviation10.448892
Coefficient of variation (CV)0.78171889
Kurtosis-0.97801004
Mean13.36656
Median Absolute Deviation (MAD)6.5551406
Skewness0.62307585
Sum164996.82
Variance109.17935
MonotonicityNot monotonic
2024-07-05T15:41:52.788666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128
 
1.0%
33.65511678 1
 
< 0.1%
9.172496659 1
 
< 0.1%
8.104388397 1
 
< 0.1%
16.24449291 1
 
< 0.1%
8.1841286 1
 
< 0.1%
10.40598437 1
 
< 0.1%
9.104272608 1
 
< 0.1%
14.2044888 1
 
< 0.1%
16.62003839 1
 
< 0.1%
Other values (12207) 12207
98.9%
ValueCountFrequency (%)
0 128
1.0%
3.311726637 × 10-51
 
< 0.1%
0.0002723611384 1
 
< 0.1%
0.000486538144 1
 
< 0.1%
0.001162506364 1
 
< 0.1%
0.001353928494 1
 
< 0.1%
0.001474690548 1
 
< 0.1%
0.001575551506 1
 
< 0.1%
0.001637908784 1
 
< 0.1%
0.002747688867 1
 
< 0.1%
ValueCountFrequency (%)
38.23409643 1
< 0.1%
37.04451075 1
< 0.1%
36.45912947 1
< 0.1%
36.38465915 1
< 0.1%
36.3407524 1
< 0.1%
36.30877408 1
< 0.1%
36.2772134 1
< 0.1%
36.26314952 1
< 0.1%
36.20298637 1
< 0.1%
36.12396057 1
< 0.1%

per_area_water
Real number (ℝ)

ZEROS 

Distinct10969
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2747293
Minimum0
Maximum27.855333
Zeros1376
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-07-05T15:41:52.988640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1340459
median0.6870386
Q31.7799364
95-th percentile4.3252929
Maximum27.855333
Range27.855333
Interquartile range (IQR)1.6458905

Descriptive statistics

Standard deviation1.7742226
Coefficient of variation (CV)1.3918426
Kurtosis22.843486
Mean1.2747293
Median Absolute Deviation (MAD)0.65160302
Skewness3.6102488
Sum15735.259
Variance3.1478657
MonotonicityNot monotonic
2024-07-05T15:41:53.188395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1376
 
11.1%
1.798143226 1
 
< 0.1%
0.245122046 1
 
< 0.1%
0.137333791 1
 
< 0.1%
0.03934790089 1
 
< 0.1%
0.388948899 1
 
< 0.1%
0.02649379018 1
 
< 0.1%
0.007574284419 1
 
< 0.1%
0.0009376593171 1
 
< 0.1%
0.3683513044 1
 
< 0.1%
Other values (10959) 10959
88.8%
ValueCountFrequency (%)
0 1376
11.1%
2.632668805 × 10-91
 
< 0.1%
4.321415953 × 10-61
 
< 0.1%
3.327274725 × 10-51
 
< 0.1%
4.429612922 × 10-51
 
< 0.1%
5.712498547 × 10-51
 
< 0.1%
0.0001076551269 1
 
< 0.1%
0.0001555083661 1
 
< 0.1%
0.0002347983729 1
 
< 0.1%
0.0003422481464 1
 
< 0.1%
ValueCountFrequency (%)
27.85533324 1
< 0.1%
20.68321406 1
< 0.1%
20.18943585 1
< 0.1%
19.94603458 1
< 0.1%
19.89993472 1
< 0.1%
19.24829146 1
< 0.1%
19.19504426 1
< 0.1%
18.80182511 1
< 0.1%
17.40978801 1
< 0.1%
17.21246959 1
< 0.1%

per_area_buildings
Real number (ℝ)

HIGH CORRELATION 

Distinct12342
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6612549
Minimum0
Maximum23.944334
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-07-05T15:41:53.405292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21044117
Q11.1647705
median4.6335796
Q36.9692729
95-th percentile11.020566
Maximum23.944334
Range23.944334
Interquartile range (IQR)5.8045024

Descriptive statistics

Standard deviation3.6032286
Coefficient of variation (CV)0.77301685
Kurtosis0.47150856
Mean4.6612549
Median Absolute Deviation (MAD)2.8243708
Skewness0.71348223
Sum57538.531
Variance12.983256
MonotonicityNot monotonic
2024-07-05T15:41:53.654981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
< 0.1%
5.869768029 1
 
< 0.1%
6.31763722 1
 
< 0.1%
1.777598535 1
 
< 0.1%
9.087423278 1
 
< 0.1%
6.812009833 1
 
< 0.1%
4.397595908 1
 
< 0.1%
5.276384326 1
 
< 0.1%
0.6309460897 1
 
< 0.1%
5.442397129 1
 
< 0.1%
Other values (12332) 12332
99.9%
ValueCountFrequency (%)
0 3
< 0.1%
0.001292989649 1
 
< 0.1%
0.003463808939 1
 
< 0.1%
0.004593450547 1
 
< 0.1%
0.005046680139 1
 
< 0.1%
0.005332112859 1
 
< 0.1%
0.006402565116 1
 
< 0.1%
0.006660210548 1
 
< 0.1%
0.007209041124 1
 
< 0.1%
0.009291787888 1
 
< 0.1%
ValueCountFrequency (%)
23.94433391 1
< 0.1%
20.90929894 1
< 0.1%
20.73901565 1
< 0.1%
20.71522834 1
< 0.1%
20.52081539 1
< 0.1%
20.44454525 1
< 0.1%
20.27873077 1
< 0.1%
20.17997013 1
< 0.1%
19.900095 1
< 0.1%
19.8617785 1
< 0.1%

per_residential_road
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10364
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.148442
Minimum0
Maximum100
Zeros278
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-07-05T15:41:53.871584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7940352
Q142.689771
median84.974609
Q397.28949
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)54.59972

Descriptive statistics

Standard deviation34.01868
Coefficient of variation (CV)0.49196598
Kurtosis-0.73681703
Mean69.148442
Median Absolute Deviation (MAD)14.994102
Skewness-0.89079535
Sum853568.36
Variance1157.2706
MonotonicityNot monotonic
2024-07-05T15:41:54.105025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1704
 
13.8%
0 278
 
2.3%
78.70989276 1
 
< 0.1%
99.52429908 1
 
< 0.1%
7.513509574 1
 
< 0.1%
8.460941539 1
 
< 0.1%
48.28523072 1
 
< 0.1%
41.89139179 1
 
< 0.1%
48.83135954 1
 
< 0.1%
27.96039028 1
 
< 0.1%
Other values (10354) 10354
83.9%
ValueCountFrequency (%)
0 278
2.3%
1.559466821 × 10-51
 
< 0.1%
9.155857978 × 10-51
 
< 0.1%
0.0004556411081 1
 
< 0.1%
0.0006497087562 1
 
< 0.1%
0.002568085952 1
 
< 0.1%
0.00276358131 1
 
< 0.1%
0.003130596088 1
 
< 0.1%
0.003584663065 1
 
< 0.1%
0.004029930874 1
 
< 0.1%
ValueCountFrequency (%)
100 1704
13.8%
99.99999967 1
 
< 0.1%
99.99999787 1
 
< 0.1%
99.99999783 1
 
< 0.1%
99.99998355 1
 
< 0.1%
99.99996971 1
 
< 0.1%
99.99996579 1
 
< 0.1%
99.99992783 1
 
< 0.1%
99.99988611 1
 
< 0.1%
99.99972194 1
 
< 0.1%

per_rural_road
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7834
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.27367
Minimum0
Maximum100
Zeros4332
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-07-05T15:41:54.305070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.1910749
Q344.073986
95-th percentile93.384385
Maximum100
Range100
Interquartile range (IQR)44.073986

Descriptive statistics

Standard deviation32.448464
Coefficient of variation (CV)1.3367762
Kurtosis-0.21934858
Mean24.27367
Median Absolute Deviation (MAD)6.1910749
Skewness1.1304619
Sum299634.19
Variance1052.9028
MonotonicityNot monotonic
2024-07-05T15:41:54.504826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4332
35.1%
100 180
 
1.5%
5.040627322 1
 
< 0.1%
95.50286803 1
 
< 0.1%
89.25345687 1
 
< 0.1%
87.11505668 1
 
< 0.1%
7.716851038 1
 
< 0.1%
62.18802785 1
 
< 0.1%
8.45908948 1
 
< 0.1%
1.774543553 1
 
< 0.1%
Other values (7824) 7824
63.4%
ValueCountFrequency (%)
0 4332
35.1%
3.286542718 × 10-71
 
< 0.1%
1.404330312 × 10-61
 
< 0.1%
2.125086909 × 10-61
 
< 0.1%
2.166527999 × 10-61
 
< 0.1%
1.645306212 × 10-51
 
< 0.1%
2.65816853 × 10-51
 
< 0.1%
3.029317791 × 10-51
 
< 0.1%
7.216638001 × 10-51
 
< 0.1%
0.0001901639591 1
 
< 0.1%
ValueCountFrequency (%)
100 180
1.5%
99.99996442 1
 
< 0.1%
99.99990844 1
 
< 0.1%
99.99954436 1
 
< 0.1%
99.99743191 1
 
< 0.1%
99.99723642 1
 
< 0.1%
99.9968694 1
 
< 0.1%
99.99641534 1
 
< 0.1%
99.99597007 1
 
< 0.1%
99.99534537 1
 
< 0.1%

per_highway
Real number (ℝ)

ZEROS 

Distinct1905
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5803672
Minimum0
Maximum82.25711
Zeros10440
Zeros (%)84.6%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-07-05T15:41:54.704697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20.608266
Maximum82.25711
Range82.25711
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.0034753
Coefficient of variation (CV)3.1016808
Kurtosis17.284974
Mean2.5803672
Median Absolute Deviation (MAD)0
Skewness3.892705
Sum31852.052
Variance64.055617
MonotonicityNot monotonic
2024-07-05T15:41:54.887942image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10440
84.6%
15.72315625 1
 
< 0.1%
17.9657421 1
 
< 0.1%
44.690452 1
 
< 0.1%
17.14876039 1
 
< 0.1%
18.99006426 1
 
< 0.1%
16.34157942 1
 
< 0.1%
14.0662307 1
 
< 0.1%
17.03725979 1
 
< 0.1%
14.39330731 1
 
< 0.1%
Other values (1895) 1895
 
15.4%
ValueCountFrequency (%)
0 10440
84.6%
3.557689311 × 10-51
 
< 0.1%
5.980732838 × 10-51
 
< 0.1%
0.0009079741161 1
 
< 0.1%
0.00147418082 1
 
< 0.1%
0.005537866347 1
 
< 0.1%
0.006242825834 1
 
< 0.1%
0.006941603343 1
 
< 0.1%
0.01030093796 1
 
< 0.1%
0.01200597143 1
 
< 0.1%
ValueCountFrequency (%)
82.25711011 1
< 0.1%
74.46662449 1
< 0.1%
73.20684594 1
< 0.1%
72.53394873 1
< 0.1%
71.52998418 1
< 0.1%
69.3804528 1
< 0.1%
69.0861014 1
< 0.1%
67.00406104 1
< 0.1%
65.04868573 1
< 0.1%
64.38220657 1
< 0.1%

per_active
Real number (ℝ)

ZEROS 

Distinct6751
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9489144
Minimum0
Maximum98.556173
Zeros5594
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size96.6 KiB
2024-07-05T15:41:55.104652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.30327271
Q34.6695335
95-th percentile18.838378
Maximum98.556173
Range98.556173
Interquartile range (IQR)4.6695335

Descriptive statistics

Standard deviation7.7318426
Coefficient of variation (CV)1.9579666
Kurtosis20.878547
Mean3.9489144
Median Absolute Deviation (MAD)0.30327271
Skewness3.7371852
Sum48745.399
Variance59.78139
MonotonicityNot monotonic
2024-07-05T15:41:55.322522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5594
45.3%
16.24947992 1
 
< 0.1%
0.9685061158 1
 
< 0.1%
4.567869157 1
 
< 0.1%
0.06263972273 1
 
< 0.1%
0.007668883908 1
 
< 0.1%
2.692970273 1
 
< 0.1%
11.4703661 1
 
< 0.1%
0.1261702002 1
 
< 0.1%
3.734472297 1
 
< 0.1%
Other values (6741) 6741
54.6%
ValueCountFrequency (%)
0 5594
45.3%
3.155746153 × 10-61
 
< 0.1%
7.629333581 × 10-61
 
< 0.1%
1.078748776 × 10-51
 
< 0.1%
1.276030494 × 10-51
 
< 0.1%
1.685857286 × 10-51
 
< 0.1%
1.969139358 × 10-51
 
< 0.1%
9.998155964 × 10-51
 
< 0.1%
0.0001138905349 1
 
< 0.1%
0.0001179909071 1
 
< 0.1%
ValueCountFrequency (%)
98.55617321 1
< 0.1%
95.80411608 1
< 0.1%
85.22295541 1
< 0.1%
84.63556676 1
< 0.1%
81.35144487 1
< 0.1%
78.91292917 1
< 0.1%
75.15056302 1
< 0.1%
74.72876896 1
< 0.1%
73.42887719 1
< 0.1%
72.80113857 1
< 0.1%

Interactions

2024-07-05T15:41:50.373139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:41.942261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:43.308696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:44.624452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:45.862098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:47.024406image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:48.157054image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:49.273387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:50.489475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:42.176091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:43.475103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:44.761591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:45.990754image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:47.174791image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:48.273589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:49.406607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:50.623082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:42.339402image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:43.708384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:45.041161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:46.124202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:47.322768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:48.423543image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:49.556725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:50.766107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:42.478779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:43.861634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:45.191418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:46.257467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:47.473593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:48.540561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:49.723007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:50.906491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:42.625166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:44.008571image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:45.323826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:46.391173image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:47.607209image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:48.690100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:49.856353image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:51.039466image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:42.759558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:44.174828image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:45.459285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:46.540461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:47.763158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:48.840226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:49.990326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:51.189725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:42.925274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:44.341535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:45.590881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:46.691872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:47.890405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:48.990014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:50.123019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:51.305629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:43.108550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:44.491505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:45.741249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:46.874106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:48.007106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:49.140069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:41:50.239796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T15:41:55.471122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
per_activeper_area_buildingsper_area_greeneryper_area_waterper_highwayper_residential_roadper_rural_roadpublictransport_frequency
per_active1.0000.054-0.0780.1310.028-0.158-0.1530.161
per_area_buildings0.0541.000-0.885-0.062-0.2670.745-0.7700.189
per_area_greenery-0.078-0.8851.000-0.0410.251-0.7310.766-0.172
per_area_water0.131-0.062-0.0411.0000.063-0.018-0.0510.077
per_highway0.028-0.2670.2510.0631.000-0.3310.1890.051
per_residential_road-0.1580.745-0.731-0.018-0.3311.000-0.8740.145
per_rural_road-0.153-0.7700.766-0.0510.189-0.8741.000-0.180
publictransport_frequency0.1610.189-0.1720.0770.0510.145-0.1801.000

Missing values

2024-07-05T15:41:51.489071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T15:41:51.722648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

publictransport_frequencyper_area_greeneryper_area_waterper_area_buildingsper_residential_roadper_rural_roadper_highwayper_active
02622.08.1243621.7981435.86976878.7098935.0406270.00000016.249480
12310.015.0306460.9411003.25491481.04720313.7008740.0000005.251923
22200.012.0828901.2138493.42707679.0438876.28622410.9689403.700950
30.030.2894920.7365280.62235829.99550343.7762810.00000026.228216
4193.027.4975930.6166170.3527976.92966078.80601312.2878061.976522
50.032.7456950.5833320.24759111.47817368.5084038.92682511.086599
61410.02.8936591.7376579.02460294.8924100.0000000.0000005.107590
70.02.5022592.7274488.016825100.0000000.0000000.0000000.000000
8941.010.3129350.2680953.88842797.7711871.2649300.0000000.963883
9381.07.7116971.0975304.19193891.6434978.3565030.0000000.000000
publictransport_frequencyper_area_greeneryper_area_waterper_area_buildingsper_residential_roadper_rural_roadper_highwayper_active
123340.034.8141691.4214200.14264366.65585033.3441330.00.000017
123350.07.5570121.5582905.63809389.52630510.4736950.00.000000
123360.032.8575241.0695120.37106242.79981657.2001840.00.000000
123375855.05.9923180.9165960.88363129.71242470.2875760.00.000000
123380.034.7465101.9982550.24067911.51939644.5521060.043.928497
123391726.08.4749281.4319344.84154994.0698970.7419150.05.188187
123400.08.3527120.4336077.11402453.2565260.0000000.046.743474
12341414.02.3726570.8723487.43503787.8067609.4397090.02.753531
12342239.016.3956742.2621313.8190060.26240399.7375970.00.000000
123430.017.3580853.8871013.68899199.8585330.1414670.00.000000